1. NOISE DETECTION AND CLASSIFICATION IN SPEECH SIGNALS WITH BOOSTING Nobuyuki Miyake, Tetsuya Takiguchi and Yasuo Ariki Department of Computer and System Engineering, Kobe University Research purpose Purpose Detecting and Classifying Sudden and Short-Period Noises Background Sudden and short-period noises often affect speech recognition system in real environments. Noise reduction improve speech recognition system. It is difficult to remove sudden and short-period noises because we do not know where the noise overlapped and what noise was. Telephone calling System overview Well, I believe that you will ・・・・ clatter Noise detection using AdaBoost Clean speech Noisy speech overlapped by sudden noises Smoothing Noise classification using AdaBoost Final results Feature extraction AdaBoost Weak classifier Classifier’s weight weak classifier We labeled learning data {-1,+1}, 1 means noisy speech data label, -1 means clean speech data label. AdaBoost is one of method of boosting. AdaBoost decides the weak classifiers and their weights. Multi-class classification using AdaBoost We perform multi-class classification using AdaBoost in order to determine noise classes. It is necessary to extend AdaBoost to classify multi-class AdaBoost class1 or other class AdaBoost class K or other class Find a maximum value in each outputs ・・・・・・ Feature vector Label of class 1 Label of class 2 Label of class 3 …. combine strong classifier If speech recognition system can detect sudden noises, it will make it possible for the system to ask the speaker to repeat the same utterance. If it can be determined what and where noise is overlapped, these information will be useful for noise reduction or model composition. Noise detection using AdaBoost AdaBoost Clean speechNoise overlapped Feature vector AdaBoost determines this frame overlapped by noise or clean speech. Learning where, weak classifier is one- dimension linear classifier. Detection AdaBoost makes strong classifier between clean speech frames and noisy speech frames using these data. Multiple two-class classifiers are created, which distinguish one class and other classes. The class of the largest value is selected from the output values. Changing η of this equation, we adjust the number of positive errors and negative errors. red blue red blue Changing data weight ・・・・・ Algorithm We use the AdaBoost for noise detection and classification because it can make complex boundary. Weight weak classifier based on performance of it Wrong data weight is bigger True data weight is smaller

2. Comparative approach We use log likelihood ratio of GMMs. It is the popular method for VAD (voice activity detection ) Detection Classification We find a class which has a maximum likelihood from noisy speech GMMs. Experiments Summary Future work Classification Noise class k Noise class １ or Other class Noise class2 or Other class Noise class K or Other class The frame to be noisy in detection approach … Noises are separated to some classes in advance. Classifiers are learned by AdaBoost to classify these classes. Learning classification Classification are applied to only the frames which are determined as noisy in detection. Classifiers decide the class of noisy speech frame. Smoothing noise １ noise2 A signal interval detected by AdaBoost may result in only a few frames Experimental condition Window size 20msec Hamming window every 10-msec Feature: 24-order log-Mel filter bank and 12-order MFCC The number of weak classifier of AdaBoost: 500 SNR of learning data : -5 dB ～ 5 dB 0.95 0.90 0.85 0.80 0.75 0.70 0.973 0.958 0.914 0.896 1.00 [SNR of 0 dB] 0.9650.962 0.9500.951 0.989 0.973 These frames are removed by smoothing. We use majority voting for smoothing. When carrying out the smoothing of one frame, the prior three and subsequent three frames are also consideration. : i-th frame’s classification output. Criteria of evaluation 0.95 0.90 0.85 0.80 0.75 0.70 0.923 0.842 0.804 1.00 [SNR of 5 dB] 0.973 0.949 0.915 0.950 0.900 0.947 0.932 Experimental results 0.95 0.90 0.85 0.80 0.75 0.70 0.974 0.973 1.00 [SNR of -5 dB] 0.9720.973 0.974 0.989 0.937 0.933 η of this equation adjust the number of positive error and negative error. We proposed the sudden noise detection and classification with Boosting. Detection and classification have high performance in low SNR. The performance using AdaBoost is better than GMM- based method. We will detect more kinds of noises combining this method with clustering method as k-means. We will combine noise detection and classification with noise reduction method. Speech data 16kHz training:210 utterances of 21 men Testing:2104 utterances of 5 men Noise data 6 kinds of noise: “spray,“ " telephone,” ”tearing paper,” “pouring of a granular substance,” “bell-ringing,” “horn” These have each 50 source. 20 data for training, 30 data for testing.